Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark is clearly ahead on the aggregate, 87 to 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3-Codex-Spark's sharpest advantage is in coding, where it averages 82.3 against 41.7. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 36.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.80 input / $4.00 output per 1M tokens for Claude Haiku 4.5. That is roughly 2.0x on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Claude Haiku 4.5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 200K for Claude Haiku 4.5.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3-Codex-Spark
85.6
Claude Haiku 4.5
56.7
GPT-5.3-Codex-Spark
82.3
Claude Haiku 4.5
41.7
GPT-5.3-Codex-Spark
88.3
Claude Haiku 4.5
78.4
GPT-5.3-Codex-Spark
92.7
Claude Haiku 4.5
68.9
GPT-5.3-Codex-Spark
78.3
Claude Haiku 4.5
53.6
GPT-5.3-Codex-Spark
92
Claude Haiku 4.5
86
GPT-5.3-Codex-Spark
90.8
Claude Haiku 4.5
80.1
GPT-5.3-Codex-Spark
96.7
Claude Haiku 4.5
73.3
GPT-5.3-Codex-Spark is ahead overall, 87 to 62. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 36.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 53.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for coding in this comparison, averaging 82.3 versus 41.7. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for math in this comparison, averaging 96.7 versus 73.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for reasoning in this comparison, averaging 92.7 versus 68.9. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for agentic tasks in this comparison, averaging 85.6 versus 56.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multimodal and grounded tasks in this comparison, averaging 88.3 versus 78.4. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for instruction following in this comparison, averaging 92 versus 86. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 80.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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